Additive Main Effects And Multiplicative Interaction Research Articles

Yield stability analysis of pearl millet genotypes in arid region of India using AMMI and GGE biplot

Aim: To assess genotypes with ability to sustain yields under arid region and to understand the significance of interactive effects of genotype × environment to develop resilient cultivars for rainfed farming. Methodology: Performances of 12 pearl millet varieties and hybrids released for commercial cultivation in India, were estimated using RBD with three replications during rainy seasons of 2018, 2019 and 2020. The trials were conducted at the Agricultural Research Station, ANGRAU, Ananthapuramu, located in the Scarce Rainfall Zone of Andhra Pradesh, India, with an average annual rainfall of nearly 553 mm and 300 mm to 400 mm during rainy season. Results: The environment and genotype effects were highly significant in the Additive Main Effect and Multiplicative Interaction (AMMI) model, implying that environments are varied and genotypes performed differently in each environment offering a great scope for selecting better adaptive genotypes. Apart from moisture stress, the amount of rainfall received during both the anthesis and grain maturation stages were influenced grain yield through plant height, 1000-grain weight and dry fodder yield. Environment 3 was the best discriminating environment and the hybrids Pratap, 86M86 and NBH 5767 had outperformed the popular open pollinated varieties in ideal conditions. Interpretation: In terms of grain yield,Pusa Comp. 612 and Pratap have proved to be reliable and had a mean higher than the general mean over environments. As a result, Pusa Comp. 612 and Pratap are suggested for large-scale assessment in a wide range of locations in rainfed situations of arid regions. Key words: GT-biplot, Grain yield, Pearl millet, Stability, Trait associations

Additive Main Effect and Multiplicative Interaction Analysis for Grain Yield of Early Maturing Sorghum [Sorghum Bicolor (L.) Moench] Varieties in Drought Prone Areas of Temben, North Ethiopia.

Shortage of widely adapted and high yielding variety is one of the major bottlenecks for production and productivity of sorghum in dry lowlands of Tigray region, Northern Ethiopia. A field experiment was conducted during the main seasons of 2018 and 2019 at four environments using randomized complete block design with three replications to evaluate the performance of ten early maturing sorghum varieties for grain yield using AMMI (Additive Main Effects and Multiplicative Interaction) model. The combined analysis of variance revealed highly significant (P≤0.01) genotype (G), environment (E) and genotype × environment interaction (GEI). The significant genotype by environment interaction effects were further partitioned in to two significant interaction principal components by using AMMI model. The AMMI analysis of variance showed that the genotype, environment and interaction sum squares contributed 41.55 %, 28.67 % and 29.78 % to the treatment sum squares for grain yield respectively. In addition, the first two IPCAs and interaction residual were significant. The first two IPCAs accounted for a total of 82.20 % of the interaction sum square. The results revealed that the observed yield variation among genotypes were due to genetic potential of genotypes and interaction rather than location differences. The highest yield was obtained from Melkam (3276 kg ha-1), while the lowest was from Grana-1 (2094 kg ha-1) and the average grain yield of genotypes was 2462 kg ha-1. Therefore, Melkam is selected as the best stable variety with consistent yielding performance across the testing environments in dry lowland areas of Temben and similar agro-ecologies in Tigray region, Northern Ethiopia.

Genetic variability, G × E interaction and stability for iron and zinc content in sorghum grains in advanced breeding lines

Iron (Fe) and Zinc (Zn) are essential micronutrients and play a major role in the growth and health of an individual. Micronutrients deficiencies can be alleviated by feeding the food grains which are biofortified. Sorghum is a staple food cereal supplying the daily nutritional requirements of the poorest of the poor in the arid and semi-arid regions of the world. Genotype × environment interaction (G × E) is major cause of inconsistency in the expression of micronutrients under different production environments. In this study, we investigated the effect of G × E on Fe and Zn along with one hundred grain weight (TW) using 20 genotypes at three target locations in India. Fe and Zn were positively correlated but negatively with TW. The AMMI (additive main effects and multiplicative interactions) analysis indicated that G × E interaction was significant for Fe and TW, and not for Zn. Two Interaction principal components IPCA1 and IPCA2 were significant for three traits and accounted for 100% of interactions. AMMI stability index (ASI) and Simultaneous Selection index (SSI) identified genotypes with least G × E. Multi-trait stability analysis identified genotypes SPV2856, SPV2631 and Phule Panchami for their superior nutritional and TW values.

Genotype recommendations for high performance and stability based on multiple traits selection across a multi-environment in rapeseed

Breeding programs for rapeseed genotypes with high yield potential and other critically targeted parameters such as high oil content and lodging resistance are important strategies for sustainable rapeseed (Brassica napus) production. However, the selection and recommendation of ideal genotypes with high performance and stability across multiple environments based on multiple traits has always been a difficult task. In this regard, two popular methods–additive main effect and multiplicative interaction (AMMI) and best linear unbiased prediction (BLUP), were adopted for analyzing the genotype × environment interaction. A superiority index (WAASBY) was introduced to integrate the mean performance and stability of the single traits. A multi–trait stability index (MTSI) was applied for genotype recommendation in combination with high performance and stability, based on multiple traits across five site–year environments. In the present multi–environment trials, the dataset (involving 41 recorded parameters assessed in 23 genotypes) was used to illustrate the application of genotype recommendations. The results showed that the reliability of the BLUP model for selecting a single parameter was assured because of the high genotypic accuracy of selection (ranging from 0.81 to 0.97). Genotype recommendations for mean performance and stability based on a single trait are partial and prejudiced, whereas selection based on multiple traits is desirable. The feasibility of MTSI application for genotype recommendations considering multiple traits was further evidenced by multiple analytical and statistical approaches. The MTSI always showed a significant and consistent relationship with WAASBY for all targeted parameters, i.e., seed yield, oil content and lodging resistance (R2 =0.26*–0.80**). Three ideal genotypes (Huayouza 50, Qingyou 3 and Zheyou 51) were selected in consideration of both the mean performance and stability of three targeted parameter, as represented by lower MTSI (1.60–1.69). This study implies that MTSI is an accurate, robust, and easy–to–handle indicator for breeders and agronomists who desire simultaneous genotype selection based on multiple traits to achieve high mean performance and stability across multiple environments.

Response of African Sorghum Genotypes for Drought Tolerance under Variable Environments

Sorghum (Sorghum bicolor (L.) Moench) is the main food staple for millions of people in Sub-Saharan Africa (SSA) and Asia. Sorghum is relatively drought tolerant and cultivated in arid and semi-arid regions under rain-fed production. However, severe drought stress often leads to crop loss and declined productivity. The development and deployment of high-yielding and drought-adapted genotypes is a cost-effective strategy for sustainable sorghum production globally. The objective of this study was to determine drought tolerance and genotype-by-environment interaction (GEI) effects on grain yields of a population of African sorghum genotypes to identify high-yielding and drought-adapted genotypes for direct production and also for use in breeding programs. Two hundred and twenty-five sorghum genotypes were evaluated under non-stressed (NS), pre-anthesis drought stress (PreADS), and post-anthesis drought stress (PoADS) conditions under field and greenhouse environments using a 15 × 15 alpha lattice design in two replicates. The three water regimes and two environments resulted in six testing environments. Data were collected on grain yield and drought tolerance parameters, and additive main effect and multiplicative interaction (AMMI) analysis were computed. The mean grain yield under NS, PreADS, and PoADS were 3.70, 1.76, and 2.58 t/ha, in that order. The best genotypes adapted to non-stressed environments were G09, and G109, whereas G114 and G56 were suitable for non-stressed and stressed conditions. G72 and G75 displayed the best performance in PreADS conditions only, whereas genotypes G210 and G12 were identified as high performers under PoADS only. The AMMI analysis revealed that genotype (G), environment (E), and GEI were significant (p < 0.05), which accounted for 38.7, 44.6, and 16.6% of the total explained variation in grain yield. AMMI 4 was the best-fitting model for grain yield. Based on AMMI 4 and the Best Linear Unbiased Estimates (BLUPs) calculations, genotypes G119 and G127 with a grain yield of 5.6 t/ha and 6.3 t/ha were selected as being suitable for non-stressed conditions. Genotypes G8 and G71 with BLUPs of 2.5 t/ha and 2.6 t/ha were best-suited for pre-anthesis drought stress conditions, whereas genotypes G115 and G120 with BLUPs of 4.2 t/ha and 4.3 t/ha are recommended for post-anthesis drought-prone environments, respectively. The identified sorghum genotypes are recommended for production in dry agro-ecologies of sub-Saharan Africa characterized by pre-and-post anthesis drought stress. In addition, the identified genotypes are valuable genetic resources to develop novel drought-tolerance material.

Dissection of genotype‐by‐environment interaction and simultaneous selection for grain yield and stability in faba bean (Vicia faba L.)

AbstractIncreasing faba bean production is indispensable to supply the growing demand for plant‐based protein on the global scale. A thorough understanding of genotype (G) × environment (E) interaction (GEI) patterns is critical to developing high‐yielding varieties with wider adaptation. Thirteen faba bean genotypes were evaluated in 15 environments during 2019–2020 in western Canada to estimate their yield stability using different stability statistics. The combined analysis of variance and additive main effects and multiplicative interaction (AMMI) analysis revealed that G, E, and GEI effects were highly significant (p < 0.001), indicating differential responses of the genotypes across the environments, enabling the stability analysis. The result of the model comparison found the best linear unbiased prediction (BLUP) to outperform AMMI models. The BLUP‐based biplot of the weighted average of absolute scores (WAASB) stability and mean grain yield (GY) identified AO 1155 (Navi), 1089‐1‐2, 1310‐5, DL Tesoro, and 1239‐1 as high‐yielding and stable genotypes. The correlation analysis revealed that most of the stability parameters had a strong association with GY and with each other, indicating that they should be used in combination with one another to select genotypes with high yield. Overall, the WAASB superiority index and the average sum of ranks of all stability statistics identified the same genotypes in terms of high‐yielding and stability, and genotype AO 1155 is considered the most stable and highest yielding among the tested genotypes. Genotypes with stable yields across environments would be beneficial for faba bean genetic improvement programs globally.

Genotype by Temperature Interaction for Plasma Physiological Indexes in Turbot (Scophthalmus maximus) under Acute Heat Stress─Exploring a Method for Screening Physiological Biomarkers of Nontoxic Stress in Aquatic Environments.

This paper presents a method for exploring the genetic mechanism underlying the plasma physiological indexes under heat stress in aquatic environments and for screening reliable stress biomarkers based on split-split-plot analysis (SSP), additive main effects and multiplicative interaction (AMMI) analysis, and genotype main effects and genotype × environment interaction (GGE) biplot analysis. The methodology developed was illustrated by applying it to a specific turbot heat stress case study. Five plasma physiological indexes (epinephrine, cortisol, alkaline phosphatase, superoxide dismutase, and blood glucose levels) were measured in turbot (Scophthalmus maximus) under acute heat stress at four temperatures (18, 21, 24, and 27 °C) for various exposure times (3, 6, 9, 12, 24, 48, and 72 h). The SSP analysis showed that exposure time and temperature × gene interactions had significant (P < 0.01) effects on the activity/content of turbot plasma physiological indexes. The AMMI analysis showed the following: (1) that at each exposure time, the genotype effect > the genotype × temperature interaction > the temperature effect; (2) that during the whole experiment, the change trend of the contribution of the genotype × temperature interactions was similar to that of the temperature effect, and the changing trends of the contributions of the genotype × temperature interaction and the genotype effect were clearly completely reversed; and (3) that the 3-24-h period was the key period for the changes in the physiological indexes due to acute heat stress. The GGE biplot analysis showed that blood glucose and cortisol levels were reliable biomarkers and could be used as early warning markers for numerical simulations of physiological behavior.

Genotype × Environment interaction using AMMI and MTSI analysis for growth and yield attributes of radish (Raphanus sativus L.) under high temperature stress conditions of North Indian plains

Thirty genotypes of Asiatic radish including nine released varieties were evaluated for four traits in six environments (sowing dates) during summer 2021 at Punjab Agricultural University, Regional Research Station, Bathinda, India. All the six sowing dates fall in summer to expose plants to varied degree of high temperature stress and included E1 (15 March), E2 (15 April), E3 (15 May), E4 (15 June), E5 (15 July) and E6 (15 August). The data were subjected to statistical analysis using Additive Main effects and Multiplicative Interaction (AMMI) and Multi-trait stability index (MTSI) models. The genotype (G) and environment (E) variances were significant for all traits. The contribution of environment to total variance was the highest for root weight (60.2%), plant weight (59.6%) and shoot weight (43.6%) whereas for shoot weight the contribution of genotype was the highest (45.0%). The G × E interaction (GEI) was significant for all traits and it was partitioned into two interaction principal components (IPCA 1 and IPCA 2). Both IPCA 1 and IPCA 2 were significant for three traits and their cumulative contribution to GEI variance was 79.9% for shoot weight, 83.4% for plant weight and 96.4% for marketable yield. However, for root weight only IPCA 1 was significant and explained 66.0% of the interaction variance. The residual variance was non-significant for all traits. The values of environmental means for various traits resulted in the identification of E1 and E6 as rich and others as poor environments. Three genotypes viz., G3 (Palak Mooli), G9 (RL-19–1) and G11 (RL-2210) were identified for poor environments based on marketable yield. One genotype viz., G9 was suitable for rich environment based on plant weight, root weight and shoot weight. Four genotypes viz., G5 (RL-9–1), G9, G17 (LSR-1–1-HP) and G3 were selected as stable due to their lower MTSI values. Six elite genotypes viz., G11, G3, G9, G2 (PMMS-1), G5 and G17 yielded 73.0 to 134.0% higher than standard check i.e. ‘Pusa Chetki’ (93.8 q/ha) and are recommended for further evaluation at multi-locations before being considered for commercial release.

Delineation of Genotype X Environment Interaction for Grain Yield in Spring Barley under Untreated and Fungicide-Treated Environments

Barley (Hordeul vulgare L.) is the fourth most important cereal crop based on production and cultivated area. Biotic stresses, especially fungal diseases in barley, are devastating, incurring high possibilities of absolute yield loss. Identifying superior and stable yielding genotypes is crucial for accompanying the increasing barley demand. However, the identification and recommendation of superior genotypes is challenging due to the interaction between genotype and environment. Hence, the present investigation was aimed at evaluating the grain yield of different sets of spring barley genotypes when undergoing one of two treatments (no treatment and fungicide treatment) laid out in an alpha lattice design in six to seven locations for five years, through additive main effects and multiplicative interaction (AMMI), GGE biplot (genotype + genotype X environment), and stability analysis. The combined analysis of variance indicated that the environment was the main factor that contributed to the variation in grain yield, followed by genotype X environment interaction (GEI) effects and genotypic effects. Ten mega environments (MEs) with five MEs from each of the treatments harboured well-adapted, stable yielding genotypes. Exploiting the stable yielding genotypes with discreet use of the representative and discriminative environments identified in the present study could aid in breeding for the improvement of grain yield in spring barley genotypes.

Stability and adaptability of wheat cultivars with low cadmium accumulation based on farmland trials

Planting wheat cultivars with low cadmium (Cd) accumulation could help overcome grain Cd contamination. However, the variation in low-Cd accumulation characteristics as influenced by genotype and the environment is unclear. To explore the stability and adaptability of wheat cultivars with low-Cd accumulation in different environments, we analyzed characteristics based on bioconcentration factor (BCF) and yield data obtained from thirteen low-Cd cultivars grown in ten locations. An additive main effect and multiplicative interaction (AMMI) model with homogeneous grouping based on location type successfully captured the effects of genotype, the environment, and their interaction on low-Cd accumulation stability, with environmental effects (49.62–68.45%) having the greatest influence on the stability of low-Cd accumulation cultivar characteristics. A genotype plus genotype-by-environment interaction (GGE) biplot accurately and intuitively illustrated the adaptability of low-Cd-accumulating wheat cultivars, indicating that the best linear unbiased prediction (BLUP) had a more plausible variation explaining the variance than the means. Here, we identify the cultivars LX99, YN999, JM229, and JM22 as having the best stability and adaptability for low-Cd accumulation by using a heterogeneous linear mixed model with BLUP calculation, combined with cluster and weight analysis. Planting these wheat cultivars with low-Cd accumulation can enable the safe utilization of lightly Cd-contaminated farmland with no reduction in yield. This study extends the application of stability evaluation methods in environmental studies and provides a reference for exploring the stability of other cultivars with low heavy metal accumulation.

Comprehensive Stability Analysis of Wheat Genotypes through Multi-Environmental Trials

In rainfed areas, due to variable environmental factors, improving the yield stability of the introduced cultivars along with increasing yield should be considered. The main aim of this study is to obtain high-yield wheat genotypes that are stable and adaptable to cold climatic conditions in Iran. For this purpose, 25 wheat genotypes were evaluated in a randomized complete blocks design with three replications during three cropping seasons (2013-2016) under supplementary irrigation and rainfed conditions. PBSTAT-GE software was used for genotype × environment interaction (GEI) analysis and comprehensive sustainability analysis. The results showed that G5, G14, G16 and G18 genotypes had good stability and general adaptation based on parametric and non-parametric stability statistics. Combined analysis of variance based on the Additive Main Effect and Multiplicative Interaction (AMMI) model showed that GEI is significant in the term of grain yield. Also, the ratios between the sum of squares G, GE and IPC1 showed that the AMMI is suitable for data analysis. GGE biplot analysis identified five mega-environments (MEs), in which ME I including E1, E2, E3, E4, E5, E6, and G7, G5, G14, G13, G16, G18, G20 being the superior ME I genotypes. According to AMMI and GGE biplote stability methods, lines G20, G18, G13, G16, G14 and Saein cultivar (G5) can be considered as desirable genetic resources in wheat production programs under variable environments in Iran, due to having the appropriate combination of yield and stability.

Evaluation of Indonesian Butterfly Pea (Clitoria ternatea L.) Using Stability Analysis and Sustainability Index

Yield and yield attributes are important components in genotypic evaluation. The butterfly pea is a native plant of Indonesia, and it is considered an underutilized crop. The goals of this study were to evaluate genotypes using environment (year) interactions (GEIs) with yield and yield attributes, and evaluate butterfly pea genotypes based on stability measurements and sustainability index (SI). The study was conducted at the Ciparanje Experimental Field, Faculty of Agriculture, Universitas Padjadjaran using 35 butterfly pea genotypes in a randomized complete block design with two replications. The field trial was conducted over three years (2018–2020). The results showed that the yield and yield attributes were influenced by GEIs. Additive main effects and multiplicative interaction (AMMI) selected 11 stable genotypes (31.43%); genotype plus genotype by environment interaction (GGE) biplot, AMMI stability value (ASV), and genotype stability index (GSI), each selected six genotypes (17.14%) that were stable and high-yielding, and SI selected 18 genotypes (51.43%) that were stable and high-yielding. There were three genotypes identified by all measurements, namely G2, G14, and G16. These three genotypes can be selected as the superior genotypes of the butterfly pea for flower production, and can be used as material for crosses in plant-breeding prog.

Impact of Genotype × Environment Interaction on Seed Yield and Pod Shattering of Soybean Genotypes in Nigeria

Yield instability and pod shattering are the major problems associated with soybean production in Nigeria. To study Genotype × Environment interaction effects on seed yield and pod shattering behaviour of some soybean genotypes in Nigeria, an experiment was conducted in three (3) environments within the country. In each environment, the experiment was laid out in a randomized complete block design (RCBD) with three replications. During the harvest, pod shattering evaluation was conducted using the sun-dry method. Data were collected on seed yield and pod shattering percentage and analyzed using Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype plus Genotype × Environment Interaction (GGE) bi-plot analyses. Genotypes NCRI SOYAC18, NCRI SOYAC78, NCRI SOYAC9, NCRI SOYAC20, NCRI SOYAC61, NCRI SOYAC22, NCRI SOYAC28 and NCRI SOYAC76, with yields above 1.23 ton/ha recorded high and stable yield across environments. For pod shattering resistance, nine genotypes (NCRI SOYAC3, NCRI SOYAC69, NCRI SOYAC77, NCRI SOYAC29, NCRI SOYAC9, NCRI SOYAC7, NCRI SOYAC67, NCRI SOYAC76 and NCRI SOYAC22) had stable pod shattering resistance across environments. Therefore, only three genotypes (NCRI SOYAC9, NCRI SOYAC22, and NCRI SOYAC76) were stable in both high yield and resistance to pod shattering. Consequently, any soybean breeding programme that involves high yield and pod shattering resistance could consider these three genotypes.

Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress

Additive main effects and multiplicative interaction (AMMI), as well as various AMMI-derived statistics, Genotype, and Genotype × Environment Interaction (GGE) models were employed on 24 sugarcane genotypes planted during two seasons (2017–18, 2018–19) under different induced salinity stress environments using saline water irrigation (iw) viz., E1 (Normal iw during crop season 2017–18), E2 (Normal iw during crop season 2018–19), E3 (4 dsm−1 ECiw during crop season 2017–18), E4 (4 dsm−1 ECiw during crop season 2018–19), E5 (8 dsm−1 ECiw during crop season 2017–18), E6 (8 dsm−1 ECiw during crop season 2018–19), E7 (12 dsm−1 ECiw during crop season 2017–18) and E8 (12 dsm−1 ECiw during crop season 2018–19) to assess the genotype by the environment interaction for the cane yield, commercial cane sugar (CCS) yield, number of millable cane (NMC), single cane weight (SCW), and pol % in juice. Individual and interactive effects of the genotype and environment for all the traits were significant. In the expression of total variability, the environmental contribution was higher for the cane yield (66.98%), CCS yield (67.60%), NMC (65.78%), and SCW (43.27%), whereas genotypic contribution was higher in the expression of pol% (82.48%). As per AMMI Stability Value (ASV), G14 (Co 13033), G23 (Co 15026), G7 (Co 05009), G17 (Co 13036), and G2 (Co 15025) were the most stable genotypes for the cane yield. Whereas as per GSI (genotype selection index), genotypes G24 (Co 15027), G21 (Co 15023), G23 (Co 15026), and G17 (Co 13036) were found most stable. The Sustainability Index (SI) of the cane yield (CY) and its contributing and CY-based computed traits were low for most of the genotypes, which indicates the negative impact of increased levels of irrigation-induced salinity in the expression of these traits. In the mean vs stability biplot analysis, G21 (Co 15023), G24 (Co 15027), G16 (Co 13036), G6 (Co 0238), and G20 (Co 14036) were found to be highly productive and stable genotypes for the cane yield. The superior and stable performance of early maturing notified varieties G21 (Co 15023) and G6 (Co 0238) for CY and CCS yield indicates that they will help the farmers to obtain sustainable income in saline soil conditions.

Use of the reversible jump Markov chain Monte Carlo algorithm to select multiplicative terms in the AMMI-Bayesian model.

The model selection stage has become a central theme in applying the additive main effects and multiplicative interaction (AMMI) model to determine the optimal number of bilinear components to be retained to describe the genotype-by-environment interaction (GEI). In the Bayesian context, this problem has been addressed by using information criteria and the Bayes factor. However, these procedures are computationally intensive, making their application unfeasible when the model's parametric space is large. A Bayesian analysis of the AMMI model was conducted using the Reversible Jump algorithm (RJMCMC) to determine the number of multiplicative terms needed to explain the GEI pattern. Three a priori distributions were assigned for the singular value scale parameter under different justifications, namely: i) the insufficient reason principle (uniform); ii) the invariance principle (Jeffreys' prior) and iii) the maximum entropy principle. Simulated and real data were used to exemplify the method. An evaluation of the predictive ability of models for simulated data was conducted and indicated that the AMMI analysis, in general, was robust, and models adjusted by the Reversible Jump method were superior to those in which sampling was performed only by the Gibbs sampler. In addition, the RJMCMC showed greater feasibility since the selection and estimation of parameters are carried out concurrently in the same sampling algorithm, being more attractive in terms of computational time. The use of the maximum entropy principle makes the analysis more flexible, avoiding the use of procedures for correcting prior degrees of freedom and obtaining improper posterior marginal distributions.

AMMI analysis for grain yield in bread wheat recombinant inbred lines

Identification of high yielding relatively stable genotypes is an integral objective of plant breeding programs. Testing of genotypes across environments is required to determine yield stability of genotypes. The specific objective of the current study was to analyze genotype by environment interaction (GEI) for grain yield of 44 bread wheat recombinant inbred lines (RILs) and six check cultivars using additive main effect and multiplicative interaction (AMMI) model. Experiments were planted using alpha lattice design with two replicates in Peshawar (E-1 and E-3), Hangu (E-2 and E-4) and Kohat (E-5) Khyber Pakhtunkhwa province, Pakistan. Analysis of variance revealed significant differences among RILs for all traits while interactions due to genotype by environment were significant for all traits except days to emergence and 1000-grain weight. Significant GEI justified environment-specific as well as AMMI analysis to identify RILs with specific and wider adaptation. The AMMI analysis revealed that the first interaction principal component analysis (IPCA 1) captured 64% of GEI sum of squares while the second interaction principal component analysis (IPCA 2) explained 25.8% of the interaction sum of square. The AMMI biplot identified G30 as high yielding genotype followed by G19 and G49, whereas low yielding RILs were G13, G8 and G7. Similarly G30, being close to IPCA1 axis, was the most stable RIL with wider adaptability followed by G31 and G25. Based on AMMI stability value (ASV), RILs G18 (2.15), G5 (2.78), G27 (3.72), G44 (4.31), G25 (4.43), G42 (4.57), G43 (5.78), G11 (5.82), G1 (7.66) and G29 (7.81) were recognized in the given order of relative stability. Stability analysis identified G49 (Wafaq ? Ghaznavi-98-3) as high yielding stable genotype among RILs which can be commercialized after fulfilling procedural requirements.

Genotype-environment Interaction for Yield and Yield Associated Traits in Chilli

The present study was conducted on six chilli genotypes across 4 environments in randomized complete block design with three replications during 2019-20 to evaluate the performance of chilli genotypes with higher yield and stability. The AMMI (Additive main effect and Multiplicative Interaction) model was used to estimate the genotype-environment interaction. Genotypes and environments were significantly varied for all the traits, which revealed the presence of genetic variability in the materials under study. The total sum of squares for genotype (G), environment (E) and G × E interactions ranged from 27.44 to 80.49%, 1.26 to 45.09% and 14.60 to 62.67% interactions respectively. The genotypes account large proportion (&gt; 50%) of total variation for Number of fruits per plant and fruit length, which means that genotype, was more important factor for these traits. GGE biplot methodology was used for graphical display of yield data after subjecting the genotypic means of each environment to GGE biplot. Principle components Analysis (PC1 and PC2) were used to depict the stability and superiority performance of the genotypes for yield and yield related traits. the chilli genotypes Co631(G6) and Co632 (G5) were stable and had comparatively less fluctuation for yield and yield related trait across environments which was found promising for recommendation and release for wider adapted variety. Cross over GEI across environment and among genotypes showed that the most general adopted environment with high mean performance, Magura has the best condition for growing chilli.

Caracterização agronômica e interação genótipo ambiente de quinoa nas condições do Brasil e da Colômbia

ABSTRACT Quinoa is a highly adaptable crop due to its considerable genetic variability, making it an important trait for cultivation under different soil and climatic conditions. To achieve crop-wide adaptation, it is essential to identify variability based on morpho-agronomic differences and genotype x environment (GxE) interaction. This study aimed to characterize eight quinoa progenies in Brazil and Colombia. The experiments in Brazil were conducted in an irrigated area of the Fazenda Água Limpa, Universidade de Brasília, at 1,100 m, on two dates: March to July 2018 and May to August 2019. In Colombia, experiments were carried out in Santander de Quilichao and Popayán at 1,100 and 1,800 m, respectively. The treatments consisted of five progenies selected in Brazil, one from Colombia, and two from Ecuador. The experiments followed a complete randomized block design, with eight progenies and four replications. For statistical analysis, the F test was used with p≤0.01 and p≤0.05. Means were grouped by the Scott-Knott test. The AMMI (Additive Main effects and Multiplicative Interaction) analysis was performed, combining analysis of variance and analysis of principal components, to adjust the main effects of genotypes (G) and environments (E) and the GxE interaction. Significant differences were found at p≤0.01 and p≤0.05 for environments, genotypes, and the interaction of GxE. The progenies with wide adaptation to environments were BRX2, BRX5, BRX6 (selected from BRS Syetetuba) and PRIX (selected from Piartal), with average yields above 3,151.95 kg/ha. The genotypes showed differences at the same location in different periods, expressing the need to carry out selection for specific periods and locations. Genotypes BRX5 and BRX6 showed high agronomic potential in all evaluated environments, being promising for future genetic improvement programs.

Herbage yield stability of cocksfoot (Dactylis glomerata L.) genotypes across rain-fed environments

Cocksfoot (Dactylis glomerata L.) is a cool-season perennial grass that naturally grows in semi-steppe rangelands in Iran. In recent years, as a result of climate change, coupled with high livestock grazing, rangeland productivity has decreased. This study aimed to analyse the stability of forage dry matter (DM) production of 36 cocksfoot genotypes across four rain-fed environments (Ardebil, Zanjan, Hamadan, and Kermanshah) in Iran. At each location, an experiment was conducted using a randomised complete-block design (RCBD) with three replications over 2 years. The result of combined analysis of variance (ANOVA) across locations showed significant effects of environment (E), genotype (G), and GE interaction (P &lt; 0.01) for DM yield. The E, G and GE interaction effects accounted for 33.19%, 17.98% and 40.52% of the total variance respectively. The GE interaction was subsequently investigated using regression stability, Additive Main effects and Multiplicative Interaction (AMMI), and genotype main effect (G) plus genotype–environment (GE) interaction (GGE) biplot analysis. According to the regression method, genotypes G2, G3, G11 and G12 with a slope close to unity, coupled with high production, had good general stability in all locations. In addition, some genotypes were specifically identified for poor- and high-performing environments. According to the AMMI-2, IPC1 vs IPC2 biplot, genotypes G2, G10, G11, G14 and G15, placed close to the origin of the biplot coupled with higher production, showed general stability in all environments. The best genotypes for the respective environments were also determined. Using the GGE biplot, genotypes G2, G3, G11 and G12 were more stable in all environments. Therefore, on the basis of all analytical methods, three local genotypes, G2 (Karaj), G3 (Marand) and G11 (Qazvin), and a foreign genotype, G14 (from, USA), were identified as most suitable genotypes for breeding improved varieties and cultivation in the study locations and similar areas.

Unravelling the G × E interactions using AMMI biplot for phenology and agro-morphological traits in linseed (Linum usitatissimum L.)

In the present study, additive main effects and multiplicative interactions (AMMI) biplot analysis was used to identify stable genotypes for days to flowering, maturity, plant height and number of branches per plant to dissect GEI in linseed (Linum usitatissimum L.). Trials were conducted in randomized complete block design (RCBD) with two replications over three consecutive years, 2016-17, 2017-18 and 2018-19. ANOVA analysis revealed genotype and G×E interaction effects contributed significant sum of square for days to flower (97.31% and 0.56%); days to maturity (89.27% and 8.90%); plant height (98.29% and 0.70%) and number of branches per plant (86.40% and 2.41% respectively). The dissection of GE interaction for all the traits was mostly explained by the first and second principal component axis (IPCA1 and IPCA2). The SSI statistic fully sync with the results of the AMMI1 biplot analysis for all the traits of top ranked genotypes across the environments. Results of genotypes stability in AMMI1 and AMMI2 biplot analyses were shown differential response with some exceptions that indicates the different sets of genes were responsible for the cumulative expression of traits under study. In the present study environments imposed variable effects with few exceptions towards the genotype stability in both AMMI1 and AMMI2 analysis models for all the traits studied. Hence, the desirable genotypes identified in linseed for phenology and agro-morphological traits could be utilized in hybridization program and varietal recommendation under semi-arid conditions.